Mutation Analysis (MutSig 2CV v3.1)
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
02 April 2015  |  analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1H1312H
Maintained by David Heiman (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Mutation Analysis (MutSig 2CV v3.1). Broad Institute of MIT and Harvard. doi:10.7908/C1H1312H
Overview
Introduction

This report serves to describe the mutational landscape and properties of a given individual set, as well as rank genes and genesets according to mutational significance. MutSig 2CV v3.1 was used to generate the results found in this report.

  • Working with individual set: KIRP-TP

  • Number of patients in set: 161

Input

The input for this pipeline is a set of individuals with the following files associated for each:

  1. An annotated .maf file describing the mutations called for the respective individual, and their properties.

  2. A .wig file that contains information about the coverage of the sample.

Summary

  • MAF used for this analysis:KIRP-TP.final_analysis_set.maf

  • Blacklist used for this analysis: pancan_mutation_blacklist.v14.hg19.txt

  • Significantly mutated genes (q ≤ 0.1): 16

Results
Lego Plots

The mutation spectrum is depicted in the lego plots below in which the 96 possible mutation types are subdivided into six large blocks, color-coded to reflect the base substitution type. Each large block is further subdivided into the 16 possible pairs of 5' and 3' neighbors, as listed in the 4x4 trinucleotide context legend. The height of each block corresponds to the mutation frequency for that kind of mutation (counts of mutations normalized by the base coverage in a given bin). The shape of the spectrum is a signature for dominant mutational mechanisms in different tumor types.

Figure 1.  Get High-res Image SNV Mutation rate lego plot for entire set. Each bin is normalized by base coverage for that bin. Colors represent the six SNV types on the upper right. The three-base context for each mutation is labeled in the 4x4 legend on the lower right. The fractional breakdown of SNV counts is shown in the pie chart on the upper left. If this figure is blank, not enough information was provided in the MAF to generate it.

Figure 2.  Get High-res Image SNV Mutation rate lego plots for 4 slices of mutation allele fraction (0<=AF<0.1, 0.1<=AF<0.25, 0.25<=AF<0.5, & 0.5<=AF) . The color code and three-base context legends are the same as the previous figure. If this figure is blank, not enough information was provided in the MAF to generate it.

CoMut Plot

Figure 3.  Get High-res Image The matrix in the center of the figure represents individual mutations in patient samples, color-coded by type of mutation, for the significantly mutated genes. The rate of synonymous and non-synonymous mutations is displayed at the top of the matrix. The barplot on the left of the matrix shows the number of mutations in each gene. The percentages represent the fraction of tumors with at least one mutation in the specified gene. The barplot to the right of the matrix displays the q-values for the most significantly mutated genes. The purple boxplots below the matrix (only displayed if required columns are present in the provided MAF) represent the distributions of allelic fractions observed in each sample. The plot at the bottom represents the base substitution distribution of individual samples, using the same categories that were used to calculate significance.

Significantly Mutated Genes

Column Descriptions:

  • nnon = number of (nonsilent) mutations in this gene across the individual set

  • npat = number of patients (individuals) with at least one nonsilent mutation

  • nsite = number of unique sites having a non-silent mutation

  • nsil = number of silent mutations in this gene across the individual set

  • p = p-value (overall)

  • q = q-value, False Discovery Rate (Benjamini-Hochberg procedure)

Table 1.  Get Full Table A Ranked List of Significantly Mutated Genes. Number of significant genes found: 16. Number of genes displayed: 35. Click on a gene name to display its stick figure depicting the distribution of mutations and mutation types across the chosen gene (this feature may not be available for all significant genes).

rank gene longname codelen nnei nncd nsil nmis nstp nspl nind nnon npat nsite pCV pCL pFN p q
1 HNRNPM heterogeneous nuclear ribonucleoprotein M 2255 167 0 0 1 0 9 0 10 10 2 8.9e-12 1e-05 0.039 3.4e-15 6.3e-11
2 NEFH neurofilament, heavy polypeptide 200kDa 3077 3 0 1 9 0 0 5 14 10 6 1.9e-07 1e-05 0.97 5.4e-11 4.9e-07
3 ZNF598 zinc finger protein 598 2763 36 0 1 10 0 0 0 10 10 1 1.5e-06 1e-05 1 4e-10 2.4e-06
4 NF2 neurofibromin 2 (merlin) 1894 12 0 1 1 2 3 4 10 10 10 1.4e-10 1 0.91 3.3e-09 0.000015
5 TDG thymine-DNA glycosylase 1269 128 0 0 1 0 4 0 5 5 3 6.5e-06 0.0029 0.24 2.6e-07 0.00096
6 SKI v-ski sarcoma viral oncogene homolog (avian) 2213 37 0 1 6 0 0 0 6 6 1 0.0017 1e-05 0.97 3.3e-07 0.001
7 MUC5B mucin 5B, oligomeric mucus/gel-forming 17492 26 0 3 13 0 1 1 15 14 11 0.0065 1e-05 0.68 1.1e-06 0.003
8 ZNF814 zinc finger protein 814 2576 5 0 2 11 0 0 3 14 8 8 0.027 1e-05 0.91 4.4e-06 0.0096
9 MET met proto-oncogene (hepatocyte growth factor receptor) 4307 15 0 0 15 0 0 1 16 15 14 0.0018 0.0035 0.0041 4.7e-06 0.0096
10 SMARCB1 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily b, member 1 1190 0 0 0 1 0 0 3 4 4 3 0.000027 0.015 0.79 7.5e-06 0.014
11 KDM6A lysine (K)-specific demethylase 6A 4318 37 0 0 0 3 1 4 8 7 8 8.6e-07 1 0.72 0.000013 0.021
12 SETD2 SET domain containing 2 7777 25 0 2 1 1 1 7 10 10 10 2.9e-06 1 0.47 2e-05 0.031
13 AHNAK2 AHNAK nucleoprotein 2 17412 23 0 4 6 1 0 1 8 7 6 0.29 1e-05 0.18 4e-05 0.057
14 AHCY S-adenosylhomocysteine hydrolase 1335 22 0 1 2 0 1 1 4 4 4 3.8e-06 1 0.77 0.000052 0.068
15 IDUA iduronidase, alpha-L- 2074 138 0 0 5 0 0 0 5 5 2 0.0012 0.002 0.99 0.000068 0.082
16 OR2L8 olfactory receptor, family 2, subfamily L, member 8 939 76 0 1 1 0 0 3 4 4 2 0.062 0.0001 0.95 8e-05 0.092
17 HOXD8 homeobox D8 877 33 0 0 2 0 0 2 4 4 3 0.00016 0.036 0.98 0.000094 0.1
18 PAM peptidylglycine alpha-amidating monooxygenase 3021 7 0 0 3 0 0 2 5 3 5 0.14 5e-05 0.47 0.00012 0.12
19 CSGALNACT2 chondroitin sulfate N-acetylgalactosaminyltransferase 2 1653 26 0 1 5 0 0 0 5 5 2 0.046 0.00038 0.82 0.00028 0.25
20 PTEN phosphatase and tensin homolog (mutated in multiple advanced cancers 1) 1244 196 0 0 2 1 0 2 5 5 5 0.000025 1 0.71 0.00029 0.25
21 FCGR2A Fc fragment of IgG, low affinity IIa, receptor (CD32) 980 48 0 0 3 0 0 1 4 4 3 0.0032 0.021 0.28 0.00029 0.25
22 BMS1 BMS1 homolog, ribosome assembly protein (yeast) 3937 66 0 1 4 0 0 0 4 4 2 0.29 0.0001 0.94 0.00033 0.27
23 BHMT betaine-homocysteine methyltransferase 1249 287 0 0 4 0 0 1 5 5 5 0.00038 1 0.053 0.00038 0.3
24 GAGE2A G antigen 2A 1486 54 0 0 0 0 0 2 2 2 1 0.0036 0.01 0.69 0.00041 0.31
25 MST1 macrophage stimulating 1 (hepatocyte growth factor-like) 2273 20 0 0 2 0 0 2 4 4 3 0.0035 0.015 0.63 0.00045 0.32
26 CUL3 cullin 3 2367 3 0 0 4 2 0 1 7 5 7 0.0011 0.038 1 0.00047 0.32
27 RPL7A ribosomal protein L7a 995 12 0 0 1 0 2 0 3 3 2 0.0028 0.029 0.081 0.00048 0.32
28 LGI4 leucine-rich repeat LGI family, member 4 1646 220 0 0 4 0 0 0 4 4 4 0.001 1 0.011 0.00049 0.32
29 PEBP1 phosphatidylethanolamine binding protein 1 576 92 0 0 3 0 0 0 3 3 3 0.0022 0.027 0.63 0.00063 0.4
30 ACTB actin, beta 1148 38 0 0 6 0 0 0 6 6 6 7e-05 1 0.79 0.00074 0.45
31 TMCO3 transmembrane and coiled-coil domains 3 2082 3 0 0 0 0 0 2 2 2 1 0.008 0.01 0.29 0.00083 0.49
32 TYRO3 TYRO3 protein tyrosine kinase 2745 96 0 0 0 0 3 0 3 3 2 0.0034 0.026 0.17 0.00098 0.56
33 SH3BP2 SH3-domain binding protein 2 1989 69 0 0 2 0 0 2 4 3 4 0.001 1 0.087 0.0011 0.61
34 SLC6A13 solute carrier family 6 (neurotransmitter transporter, GABA), member 13 1865 125 0 0 4 0 0 0 4 4 4 0.001 1 0.086 0.0011 0.61
35 FAT1 FAT tumor suppressor homolog 1 (Drosophila) 13871 5 0 1 9 0 0 6 15 13 15 0.0078 0.03 0.25 0.0013 0.69
Methods & Data
Methods

In brief, we tabulate the number of mutations and the number of covered bases for each gene. The counts are broken down by mutation context category: four context categories that are discovered by MutSig, and one for indel and 'null' mutations, which include indels, nonsense mutations, splice-site mutations, and non-stop (read-through) mutations. For each gene, we calculate the probability of seeing the observed constellation of mutations, i.e. the product P1 x P2 x ... x Pm, or a more extreme one, given the background mutation rates calculated across the dataset. [1]

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References
[1] TCGA, Integrated genomic analyses of ovarian carcinoma, Nature 474:609 - 615 (2011)
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